NDLI: Sparse Logistic Discriminant Analysis

Content Provider	IEEE Xplore Digital Library
Author	Kurita, T. Watanabe, K. Hidaka, A.
Copyright Year	2013
Description	Author affiliation: Sch. of Sci. & Eng., Tokyo Denki Univ., Tokyo, Japan (Hidaka, A.) \|\| Dept. of Comput. & Commun. Sci., Wakayama Univ., Wakayama, Japan (Watanabe, K.) \|\| Dept. of Inf. Eng., Hiroshima Univ., Higashi-Hiroshima, Japan (Kurita, T.)
Abstract	Linear discriminant analysis (LDA) is a well-known method to extract efficient features for multi-class classification. Otsu derived the optimal (ultimate) non-linear discriminant analysis (ONDA) by supposing underlying probabilities and showed that ONDA was closely related to Bayesian decision theory (posterior probabilities). Also Otsu pointed out that the usual LDA could be regarded as the linear approximation of this ultimate ONDA through the linear approximations of the Bayesian posterior probabilities. This theory of ONDA suggests that we can construct a novel nonlinear discriminant mapping by utilizing the estimates of the posterior probabilities. Based on this theory, logistic discriminant analysis (LgDA) was proposed by one of the authors as the approximation of ONDA. In LgDA, the posterior probabilities are estimated by logistic regression. In this paper, we propose the sparse logistic discriminant analysis in which the posterior probabilities are estimated by the sparse logistic regression with L2-or L1-regularizer to improve the generalization performance of LgDA further. Experiments using the standard datasets for classification reveal that the discriminant spaces by our proposed method (LgDA-L2 and LgDA-L1) are better than those by LDA and LgDA in terms of the recognition rates for test samples.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	3003
Ending Page	3008
File Size	518847
Page Count	6
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Logistics Linear approximation Bayes methods Covariance matrices Eigenvalues and eigenfunctions Regularization Discriminant Analysis Logistic Regression
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in